{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T05:55:26Z","timestamp":1772690126680,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ship recognition with optical remote sensing images is currently widely used in fishery management, ship traffic surveillance, and maritime warfare. However, it currently faces two major challenges: recognizing rotated targets and achieving fine-grained recognition. To address these challenges, this paper presents a new model called Related-YOLO. This model utilizes the mechanisms of relational attention to stress positional relationships between the components of a ship, extracting key features more accurately. Furthermore, it introduces a hierarchical clustering algorithm to implement adaptive anchor boxes. To tackle the issue of detecting multiple targets at different scales, a small target detection head is added. Additionally, the model employs deformable convolution to extract the features of targets with diverse shapes. To evaluate the performance of the proposed model, a new dataset named FGWC-18 is established, specifically designed for fine-grained warship recognition. Experimental results demonstrate the excellent performance of the model on this dataset and two other public datasets, namely FGSC-23 and FGSCR-42. In summary, our model offers a new route to solve the challenging issues of detecting rotating targets and fine-grained recognition with remote sensing images, which provides a reliable foundation for the application of remote sensing images in a wide range of fields.<\/jats:p>","DOI":"10.3390\/rs16010130","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T09:35:21Z","timestamp":1703756121000},"page":"130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Recognition Model Incorporating Geometric Relationships of Ship Components"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4637-8030","authenticated-orcid":false,"given":"Shengqin","family":"Ma","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Wenzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-3300","authenticated-orcid":false,"given":"Zongxu","family":"Pan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yuxin","family":"Hu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Guangyao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1266-0324","authenticated-orcid":false,"given":"Qiantong","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, J., Li, Z., Chen, M., Wang, Y., and Luo, Q. (2022). A new ship detection algorithm in optical remote sensing images based on improved R3Det. Remote Sens., 14.","DOI":"10.3390\/rs14195048"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, F., Liu, J., Dong, C., and Wang, X. (2017). Ship detection in optical remote sensing images based on wavelet transform and multi-level false alarm identification. Remote Sens., 9.","DOI":"10.3390\/rs9100985"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3091","DOI":"10.1109\/TGRS.2017.2658950","article-title":"Inshore ship detection in remote sensing images via weighted pose voting","volume":"55","author":"He","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1109\/LGRS.2016.2618385","article-title":"A novel inshore ship detection via ship head classification and body boundary determination","volume":"13","author":"Li","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8458","DOI":"10.1109\/JSTARS.2021.3104230","article-title":"ShipRSImageNet: A large-scale fine-grained dataset for ship detection in high-resolution optical remote sensing images","volume":"14","author":"Zhang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/81.222795","article-title":"The CNN paradigm","volume":"40","author":"Chua","year":"1993","journal-title":"IEEE Trans. Circuits Syst. Fundam. Theory Appl."},{"key":"ref_7","unstructured":"Etten, A. (2018). You only look twice: Rapid multi-scale object detection in satellite imagery. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5512","DOI":"10.1109\/TGRS.2019.2899955","article-title":"R2-CNN: Fast Tiny object detection in large-scale remote sensing images","volume":"57","author":"Pang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","first-page":"3163","article-title":"R3det: Refined single-stage detector with feature refinement for rotating object","volume":"35","author":"Yang","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10015","DOI":"10.1109\/TGRS.2019.2930982","article-title":"CAD-Net: A context-aware detection network for objects in remote sensing imagery","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wu, Y., Ma, W., Gong, M., Bai, Z., Zhao, W., Guo, Q., Chen, X., and Miao, Q. (2020). A coarse-to-fine network for ship detection in optical remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12020246"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., Zhang, H., Dong, Y., and Wei, S. (2019). Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050531"},{"key":"ref_13","first-page":"5217712","article-title":"A robust one-stage detector for multiscale ship detection with complex background in massive SAR images","volume":"60","author":"Yang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","first-page":"5624214","article-title":"MULS-Net: A Multilevel Supervised Network for Ship Tracking From Low-Resolution Remote-Sensing Image Sequences","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"5610013","article-title":"Progressive Task-based Universal Network for Raw Infrared Remote Sensing Imagery Ship Detection","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5629115","DOI":"10.1109\/TGRS.2022.3201530","article-title":"COCO-Net: A Dual-Supervised Network With Unified ROI-Loss for Low-Resolution Ship Detection From Optical Satellite Image Sequences","volume":"60","author":"Xu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhu, X., Wang, X., Yang, S., Li, W., Wang, H., Fu, P., and Luo, Z. (2017). R2CNN: Rotational region CNN for orientation robust scene text detection. arXiv.","DOI":"10.1109\/ICPR.2018.8545598"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. (2019, January 15\u201320). Learning RoI transformer for oriented object detection in aerial images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_20","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (November, January 27). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, X., and Yan, J. (2020, January 23\u201328). Arbitrary-oriented object detection with circular smooth label. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Proceedings, Part VIII 16.","DOI":"10.1007\/978-3-030-58598-3_40"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., and Metaxas, D. (2021, January 5\u20139). Oriented object detection in aerial images with box boundary-aware vectors. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Virtual.","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., and Han, J. (2021, January 11\u201317). Oriented R-CNN for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_24","first-page":"5612414","article-title":"Arbitrary-oriented ship detection through center-head point extraction","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","first-page":"4700714","article-title":"TCD: Task-collaborated detector for oriented objects in remote sensing images","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1109\/TIP.2019.2947792","article-title":"Combining faster R-CNN and model-driven clustering for elongated object detection","volume":"29","author":"Fang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5620314","DOI":"10.1109\/TGRS.2022.3162195","article-title":"An explainable attention network for fine-grained ship classification using remote-sensing images","volume":"60","author":"Xiong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4929","DOI":"10.1109\/TGRS.2019.2894425","article-title":"Multisource region attention network for fine-grained object recognition in remote sensing imagery","volume":"57","author":"Sumbul","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","unstructured":"Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Wong, C., Yifu, Z., and Montes, D. (2022). ultralytics\/yolov5: v6. 2-yolov5 classification models, apple m1, reproducibility, clearml and deci. ai integrations. Zenodo."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7665","DOI":"10.1109\/JSTARS.2022.3204578","article-title":"A generating-anchor network for small ship detection in SAR images","volume":"15","author":"Yue","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nielsen, F., and Nielsen, F. (2016). Introduction to HPC with MPI for Data Science, Springer.","DOI":"10.1007\/978-3-319-21903-5"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hu, H., Gu, J., Zhang, Z., Dai, J., and Wei, Y. (2018, January 18\u201323). Relation networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00378"},{"key":"ref_34","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1109\/LGRS.2016.2565705","article-title":"Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds","volume":"13","author":"Liu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1109\/JSTARS.2020.2981686","article-title":"A new benchmark and an attribute-guided multilevel feature representation network for fine-grained ship classification in optical remote sensing images","volume":"13","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Di, Y., Jiang, Z., and Zhang, H. (2021). A public dataset for fine-grained ship classification in optical remote sensing images. Remote Sens., 13.","DOI":"10.3390\/rs13040747"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nabati, R., and Qi, H. (2019, January 22\u201325). Rrpn: Radar region proposal network for object detection in autonomous vehicles. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803392"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1007\/s11263-022-01593-w","article-title":"On the arbitrary-oriented object detection: Classification based approaches revisited","volume":"130","author":"Yang","year":"2022","journal-title":"Int. J. Comput. Vis."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:43:20Z","timestamp":1760132600000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010130"],"URL":"https:\/\/doi.org\/10.3390\/rs16010130","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,28]]}}}